Biogeosciences, 9, 1957–1967, 2012
www.biogeosciences.net/9/1957/2012/
doi:10.5194/bg-9-1957-2012
© Author(s) 2012. CC Attribution 3.0 License.
Biogeosciences
Oxygen exchange and ice melt measured at the ice-water interface
by eddy correlation
M. H. Long1 , D. Koopmans1 , P. Berg1 , S. Rysgaard2,3 , R. N. Glud2,4,5 , and D. H. Søgaard2,5
1 Department
of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
Climate Research Centre, Greenland Institute of Natural Resources, Nuuk, Greenland
3 Centre for Earth Observation Science, CHR Faculty of Environment Earth and Resources, University of Manitoba,
Winnipeg, Canada
4 Scottish Association of Marine Sciences, Oban, Scotland, UK
5 Southern Danish University and NordCee, Odense M, Denmark
2 Greenland
Correspondence to: P. Berg (pb8n@virginia.edu)
Received: 28 October 2011 – Published in Biogeosciences Discuss.: 23 November 2011
Revised: 13 April 2012 – Accepted: 24 April 2012 – Published: 4 June 2012
Abstract. This study examined fluxes across the ice-water
interface utilizing the eddy correlation technique. Temperature eddy correlation systems were used to determine rates
of ice melting and freezing, and O2 eddy correlation systems were used to examine O2 exchange rates driven by biological and physical processes. The study was conducted
below 0.7 m thick sea-ice in mid-March 2010 in a southwest Greenland fjord and revealed low rates of ice melt at
a maximum of 0.80 mm d−1 . The O2 flux associated with
release of O2 depleted melt water was less than 13 % of
the average daily O2 respiration rate. Ice melt and insufficient vertical turbulent mixing due to low current velocities caused periodic stratification immediately below the ice.
This prevented the determination of fluxes 61 % of the deployment time. These time intervals were identified by examining the velocity and the linearity and stability of the
cumulative flux. The examination of unstratified conditions
through vertical velocity and O2 spectra and their cospectra revealed characteristic fingerprints of well-developed
turbulence. From the measured O2 fluxes a photosynthesis/irradiance curve was established by least-squares fitting.
This relation showed that light limitation of net photosynthesis began at 4.2 µmol photons m−2 s−1 , and that algal communities were well-adapted to low-light conditions as they
were light saturated for 75 % of the day during this early
spring period. However, the sea-ice associated microbial and
algal community was net heterotrophic with a daily gross primary production of 0.69 mmol O2 m−2 d−1 and a respiration
rate of −2.13 mmol O2 m−2 d−1 leading to a net ecosystem
metabolism of −1.45 mmol O2 m−2 d−1 . This application of
the eddy correlation technique produced high temporal resolution O2 fluxes and ice melt rates that were measured without disturbing the in situ environmental conditions while integrating over an area of approximately 50 m2 which incorporated the highly variable activity and spatial distributions
of sea-ice communities.
1
Introduction
Sea-ice associated microbial communities can be an important component of marine polar food webs (Arrigo 2003)
and when sea-ice is present it can host 30–65 % of the
ecosystem primary production (e.g. Mikkelsen et al., 2008;
McMinn et al., 2010). Primary production and phototrophic
growth in sea-ice is largely limited by the amount of light
that transmits through the ice and snow cover (Arrigo, 2003;
McMinn et al., 2010). Therefore production is tightly linked
to photosynthetic efficiency, with sea-ice algae being welladapted to low light conditions (Mock and Gradinger, 1999;
Kuhl et al., 2001; Rysgaard et al., 2008). Oxygen (O2 )
fluxes are commonly used to determine photosynthetic production and respiration in aquatic ecosystems, however at
the ice-water interface such measurements are complicated
by an additional O2 flux contribution driven by the physical processes of melting and freezing (Glud et al., 2002).
Published by Copernicus Publications on behalf of the European Geosciences Union.
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Specifically, sea-ice formation can cause a release of O2 supersaturated brines, while sea-ice melt can generate a release
of O2 undersaturated melt water (Glud et al., 2002; Rysgaard
et al., 2008), confounding measurements of primary production and respiration during periods of rapid freeze or melt.
Therefore, to estimate the biotic O2 flux it is necessary to
quantify the physical contribution from freezing and melting.
Sampling at the ice-water interface presents a number of
challenges for traditional techniques for quantifying primary
production and respiration. The techniques presently used to
study sea-ice primary production sample at either one point
on the ice surface or within an ice core, though algal patchiness and activity have been reported to vary across a much
wider range (Gosselin et al., 1986; Rysgaard et al., 2001).
This may lead to misrepresentation of the actual sea-ice environment and requires that many replicates are taken over
a large area. A further limitation of conventional methods is
their poor resolution of temporal variation, which has limited the studies of short-term variations in production and
respiration in sea-ice microbial communities. The 14 C tracer
method and 3 H labeled thymidine approach are the most
widely applied techniques to determine primary production
and bacterial production in sea-ice, respectively. However,
both methods are time consuming, making multiple samples
and a wide spatial coverage difficult (McMinn and Ashworth,
1998; Søgaard et al., 2010). Both approaches also require
melting the ice to evenly distribute the tracers, which grossly
changes the natural conditions experienced by the microalgae and bacteria (e.g. nutrient levels, light, and O2 gradients)
(Glud et al., 2002; Rysgaard et al., 2008).
Pulse amplitude modulated (PAM) fluorometry represents
a promising alternative to study the primary production of
natural communities. However, when PAM is applied to intact ice cores the depth integrated signal is poorly defined and
depends on variations in algae biomass as well as the optical
properties of the ice (Rysgaard et al., 2001; Glud et al., 2002).
The conversion of PAM data into carbon (or O2 ) fluxes further complicates the method as frequent intercalibration to
poorly constrained empirical relationships are required. Microsensor profiling using both Clark-type O2 microsensors
and O2 micro-optodes has also been used to examine icewater O2 fluxes but can suffer from calibration problems
due to temperature and salinity gradients and spatial microvariability in the ice (McMinn and Ashworth, 1998; Glud et
al., 2002; Mock et al., 2003; McMinn et al., 2007).
Eddy correlation in aquatic environments is a technique
typically used for measuring fluxes across the sedimentwater interface by correlating high temporal resolution measurements of velocity and a scalar quantity of interest (Berg
et al., 2003). Oxygen eddy correlation systems have been
used to determine photosynthesis and respiration in muddy
marine benthic systems and river bottoms (Berg et al., 2003),
sandy sediments (Kuwae et al., 2006; Berg and Huettel,
2008; Reimers et al., 2012), the deep ocean (Berg et al.,
2009), lakes (Brand et al., 2008; McGinnis et al., 2008;
Biogeosciences, 9, 1957–1967, 2012
Lorrai et al., 2010), hard-bottom arctic substrates (Glud et
al., 2010), and seagrass beds (Hume et al., 2011). With the
eddy correlation technique measurements are conducted under true in situ conditions, i.e. without disturbing the surface
and under natural light and hydrodynamic conditions (Berg
et al., 2003). The technique also integrates over a large surface area, typically tens of meters long and a few meters wide
(Berg et al., 2007). For these reasons, the technique is ideal
for applications under sea-ice allowing fluxes across the icewater interface to be examined non-invasively. The eddy correlation technique has been used to determine fluxes of momentum, heat, and salt beneath sea-ice (McPhee, 1992; Shirasawa et al., 1997; Widell et al., 2006; McPhee et al., 2008)
but has not previously been used to quantify O2 exchange
and thereby production and respiration of sea-ice microbial
communities. In this study we present results obtained with
O2 eddy correlation systems and temperature eddy correlation systems that are used collectively to determine true in
situ O2 exchange rates and thereby non-invasively determine
the primary production and respiration of sea-ice algal communities.
2
2.1
Methods
Site description
Measurements were performed in a small tributary fjord near
Kapisigdlit, Greenland from 12–17 March, 2010. The subArctic fjord is located in southwest Greenland and is part
of the Nuup Kangerlua Fjord. The fjord was partially covered with ice at a thickness of ∼70 cm and a snow cover
of ∼2 cm during the sampling period. Measurements were
conducted beneath the ice, 100 m from open water. Air temperatures during the study period ranged from −8.8 ◦ C to
+2.9 ◦ C with an average temperature of −3.2 ◦ C. Water depth
at the deployment location varied between 40 and 45 m. The
concentration of chlorophyll a in the bottom 12 cm of ice
was 2.8 ± 0.4 µg L−1 (SE, n = 3) and the average concentration across the entire ice thickness was 1.0 ± 1.2 µg L−1 (SE,
n = 3).
2.2
Eddy correlation measurements
Two O2 eddy correlation systems and two temperature eddy
correlation systems were deployed beneath the ice. The O2
eddy correlation system consists of an acoustic Doppler Velocimeter (ADV) (Nortek AS, Norway) that has been modified to record measurements made with a high-resolution
custom-made pA amplifier (Max Planck Institute for Marine
Microbiology, Germany) to which a fast responding (<0.2 s)
Clark-type O2 microsensor is mounted (Berg and Huettel,
2008, Berg et al., 2009; Hume et al., 2011). The temperature
eddy correlation system consists of a standard ADV coupled
to a rapid-response (<0.1 s) temperature and conductivity
sensor (Fast CT sensor, Precision Measurement Engineering,
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Fig. 1. Eddy correlation instrument deployed under sea-ice through
a rectangular-shaped hole and secured with a T bar at the ice-air interface. The instrument may freeze into the ice but the power supply
(top left) and communication port (top center) remained accessible
from above.
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component of the O2 concentration or temperature, and the
bars symbolize averaging over time (Berg et al., 2003). The
fluctuating components were separated from the means by
Reynolds decomposition as u0z = uz − uz and C 0 = C − C
where uz and C are the instantaneous measurements, and the
means (uz and C) were determined by linear least squares
fits (linear detrending) over each 0.25 h interval (Berg et al.,
2009). The three-dimensional velocity field was rotated to
orient the x-axis into the mean flow direction and to bring
the mean vertical velocities to zero to correct for any sensor tilt. The O2 concentration, temperature, and velocities in
each interval were examined carefully for anomalous variations due to sensor malfunctions caused by disturbances of
the sensors, for example due to debris floating in the water.
Furthermore, the mean current velocity, the cumulative flux
(O2 or heat), the spectra (vertical velocity and heat or O2
concentration), and the cospectra (vertical velocity vs. heat
or O2 concentration) were used to identify periods with welldeveloped turbulence under the ice where trustworthy eddy
fluxes could be determined (see Results and Discussion).
The rate of ice melt (h) was calculated from the temperature flux (Eq. 1) as:
h =
u0z T 0 cpw ρw
1Hf ρi
(2)
USA) (Crusius et al., 2008). Damage to the conductivity sensors prevented simultaneous measurement of the salt flux.
The systems were positioned under the sea-ice by lowering them through a 0.25 by 0.60 m rectangular hole (Fig. 1).
This new deployment tactic, compared to traditional tripod
mounted systems, was very simple and did not require any
diver support. The ADV and scalar sensors were mounted to
a stainless steel T rod with the sensors positioned at the edge
of the ADV’s ∼1 cm3 measuring volume that was located
22 cm below the ice-water interface.
Water velocity (x, y, and z components), time, O2 concentration and temperature were recorded by the ADVs at
a frequency of 64 Hz in intervals of 0.25 h. Total measurement periods were typically 24 h long to capture diurnal fluctuations. An O2 optode (Hach, USA) measured the mean
O2 concentration as a calibration check of the Clark-type
O2 microsensors. Photosynthetically active radiation (PAR)
was determined using a self-contained cosine-corrected integrating PAR logger (Odyssey, New Zealand) which was deployed just below the ice-water interface.
where cpw is the specific heat capacity of seawater, ρ w is
the density of seawater, 1H f is the specific heat of fusion
of ice, and ρ i is the density of the ice. The numerator of
Eq. (2) constitutes the heat flux. For the purposes of this first
order calculation of the melt rate we assumed the conduction of heat up through the ice was negligible. This assumption makes the calculated rates represent an upper limit for
ice melt. Furthermore, due to the difficulty in determining
the O2 concentration of the melt water, an O2 flux due to
ice melt was determined assuming that the melt water from
sea-ice was anoxic. This also results in an overestimate of
the O2 flux due to ice melt as bulk ice O2 concentrations
were found to range between 40 and 280 µmol L−1 at a bulk
salinity of 4.6 during the campaign (Tison et al., unpublished
data). Assuming a small, constant contribution of ice melt to
the total O2 exchange rate, a constant respiration rate, and
that O2 metabolism occured predominantly at the ice-water
interface, simple relationships between measured O2 fluxes,
rates of O2 production (P ) and respiration (R), and the irradiance were developed:
2.3
O2 flux = P − R
(3)
P = P0 (1 − exp(−I /Ik ) )
(4)
Data analysis
Fluxes of O2 and heat across the ice-water interface were
calculated from high resolution measurements of the vertical
velocity and either the O2 concentration or temperature as:
Flux = u0z C 0
(1)
where u0z is the instantaneous fluctuating component of
the vertical velocity, C 0 is the instantaneous fluctuating
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where P0 is the maximum primary production rate in the photosynthesis to irradiance (P /I ) curve for sea-ice algae, I is
irradiance and Ik is the photoadaptation parameter or the irradiance level at which photosynthesizing organisms begin
to become saturated with light (Platt et al., 1980; Smith et
al., 1988; Suzuki et al., 1997).
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Fig. 2. (A) shows the mean current velocity measured 22 cm below the ice-water interface over 4.5 d. The dark shaded region (<1 cm s−1 )
represents conditions where density stratification in the water column appeared to be persistently present while the lightly shaded region (1–
2 cm s−1 ) represents conditions where stratification was occasionally observed. The presence of stratification was evaluated by examining
the mean velocity and the stability and linearity of the cumulative flux signal. (B) shows a 12 h segment of the velocity data in (A). (C) shows
that during this segment the flux signal was strongly dependent on current velocity. The magnitude of the vertical rise at hour 2.3, when
stratification eroded due to an increase in velocity, was not indicative of any true flux and therefore was not included in the flux measurements.
3
Results
During periods of low current flow, vertical turbulent mixing was not consistently adequate to prevent density stratification from forming immediately below the ice. However,
well-developed turbulence was generally found at current velocities greater than 1–2 cm s−1 . Figure 2 shows an example
of how current velocity changed the turbulent transport and
mixing under ice. At low current velocities there was little or
no vertical mixing, which resulted in the formation of density stratification. As the velocity increased there was a point
when this stratification was eroded and allowed the water
column immediately below the ice to be mixed. The onset
of mixing events were evident in large O2 and temperature
fluctuations and were not used in the flux calculations because of the incompatibility with linear detrending over these
periods of widely fluctuating O2 and temperature signals.
Furthermore, the mixing of the stratified layer was not indicative of the actual flux at that point in time, and the period
leading up to this integrated signal is poorly constrained. The
erosion of stratification was followed by periods of stable
Biogeosciences, 9, 1957–1967, 2012
and linear cumulative flux signals that were suitable for flux
determination. Finally, as the velocity and associated turbulence decreased, the stratification was re-established. Stratification prevented the determination of fluxes 61 % of the total
deployment time.
Stratified conditions were identified by examining the current velocity and the corresponding cumulative flux signal. Under low flow conditions the cumulative flux became
highly nonlinear and erratic, indicating a weak flux signal.
Under high flow conditions a consistent and linear trend
in the cumulative flux indicated a strong flux signal. Wellmixed conditions where fluxes could be accurately determined were found 26 % of the time. Due to broken or malfunctioning sensors, fluxes were not determined during the
remaining 13 % of the time despite likely turbulent conditions (i.e. velocity >2 cm s−1 ). Unstratified conditions were
further examined in the frequency domain through the vertical velocity spectrum, where fully developed turbulence was
indicated by a characteristic −5/3 slope (Fig. 3a) (Kundu and
Cohen, 2008). Similarly the O2 spectrum shows an initial
−5/3 slope at lower frequencies and the expected −1 slope
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
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Fig. 3. Typical spectra for current-driven turbulent flow under sea-ice (current velocity: 1.86 cm s−1 ). (A) and (B) show the normalized
spectra of the vertical velocity and the O2 concentration, respectively. The −5/3 slopes are indicative of well-developed turbulence and
represent the inertial subrange where inertial forces dominate transport, according to Kolmogorov’s theory of turbulence. The −1 slope
at higher frequencies in the O2 spectrum indicates the transport of O2 by both viscous and convective forces. (C) shows the normalized
cumulative cospectrum of vertical velocity and O2 and revealed that eddies with frequencies <∼0.1 Hz contributed most of the vertical
turbulence-driven O2 transport.
Fig. 4. A typical example of eddy correlation data measured in 0.25 h intervals 22 cm below the ice-water interface through 2.5 h. (A) shows
the x, y, and z components of the velocity at 16 Hz and the mean current velocity. (B) shows the corresponding O2 concentration at 16 Hz.
(C) shows the cumulative flux calculated for each 0.25 h interval and (D) is the average O2 flux for each 0.25 h interval. A negative flux is a
net flux of O2 towards the ice. The black bar is the O2 flux averaged across 2.5 h where the error bar represents the standard error.
at higher frequencies where viscous forces gain importance
(Fig. 3b) (Kundu and Cohen, 2008). The normalized cumulative cospectrum of the vertical velocity and O2 concentration
(Fig. 3c) also shows the typical fingerprint of turbulent transport where eddies with frequencies less than 0.1 Hz dominate
the vertical turbulent transport of O2 . Under well-mixed conditions the fluxes of O2 and heat were highly reproducible.
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For example, Fig. 4 shows a typical example of O2 eddy
correlation data at night over 2.5 h, with an average flux of
−2.99 ± 0.26 mmol m−2 d−1 (SE, n = 10) where a negative
flux is a net flux of O2 towards the ice. The consistent and linear cumulative fluxes indicate a strong flux signal (Fig. 4c).
All trustworthy mean O2 fluxes for the entire study period
are shown in Fig. 5. The smallest net O2 flux towards the
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Fig. 6. P/I curve calculated by fitting Eqs. 3 and 4 simultaneously
(R 2 = 0.923) to the averaged O2 fluxes in Fig. 5a. The open circles
represent the GPP determined from the averaged fluxes in Fig. 5A.
This P/I curve had an Ik value of 4.2 µmol photons m−2 s−1 which
represents the point where light saturation begins to occur.
Fig. 5. Averaged O2 fluxes for periods with well-developed turbulence under the ice-water interface are shown in (A), where n represents the number of 0.25 h intervals included in each average. The
open circles in (A) represent the flux determined by fitting Eqs. 3
and 4 simultaneously (R 2 = 0.923) to the averaged fluxes. B, C, D,
and E show the averages of under ice irradiance, mean current velocity, temperature and current angle over each period, respectively.
All error bars represent standard errors.
ice occurred during peak irradiance. No correlation between
current direction and the O2 flux was found (R 2 = 0.021, p
= 0.816, n = 123). The white circles in Fig. 5a are the model
results determined by simultaneously fitting Eqs. 3 and 4 (R 2
= 0.923) to the mean fluxes as described below.
The P/I curve (Eq. 4) resulted from the fitting of the
eddy correlation data in Fig. 5, with R being the average O2 flux in the dark (R 2 = 0.923, Fig. 5). The maximum P rate was 1.76 ± 0.27 mmol O2 m−2 d−1 (SE, n =
8) and Ik was 4.2 µmol photons m−2 s−1 (based on irradiance measured just below the ice-water interface). Using
Eqs. 3 and 4 and the measured light values, 0.25 h O2 exchange rates were calculated across 4 days of the study
period (Fig. 7). From these model results the daily values
of R, gross primary production (GPP), and net ecosystem
Biogeosciences, 9, 1957–1967, 2012
metabolism (NEM) were calculated with the rates weighted
by the hours of daylight and darkness and assuming that
respiration was the same day and night. This resulted in a
value of R of −2.13 mmol O2 m−2 d−1 while GPP averaged
0.69 ± 0.02 mmol O2 m−2 d−1 (SE, n = 4), and NEM averaged −1.45 ± 0.02 mmol O2 m−2 d−1 (SE, n = 4). Light saturation (based on Ik ) occurred at an average of 1.47 ± 0.09 h
(SE, n = 8) after first light and before darkness. With an average day length of 11.69 ± 0.24 h (SE, n = 4) the bottom ice
algal communities were therefore light saturated for 75 % of
the day.
The mean heat flux was 2.83 ± 0.30 W m−2 (SE, n
= 31), giving a calculated rate of ice melt (Eq. 2) of
0.80 ± 0.09 mm d−1 (SE, n = 31). The mean observed
temperature of seawater during the measuring period was
−0.05 ◦ C and the salinity 33. The specific heat capacity of
the seawater was assumed to be 3.999 J g−1 ◦ C−1 and the
density was assumed to be 1.026 g cm−3 (Fofonoff and Millard, 1983). In our first-order calculation of melting rates we
assumed that the entire heat flux towards the sea-ice was
used to melt pure ice, and thus, neglected any portion of the
flux that might have been lost by heat conduction up into
the ice interior. As explained above, this leads to an overestimation of the ice melt rate and of the O2 flux associated with it. Based on this assumption we used a heat of
fusion for pure ice of 333.6 J g−1 (Harvey, 2010) and a density of ice of 0.917 g cm−3 (Eicken, 2003). The average ice
melt rates shown in Fig. 8 varies from 0.49 ± 0.11 (SE, n
= 6) to 0.99 ± 0.25 mm d−1 (SE, n = 8). The ice melt rates
were not significantly different between each period (F4 =
2.040, p = 0.111 at 0.01 level). There were no correlations
between the observed 0.25 h heat fluxes and velocity (R 2 =
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
4
Fig. 7. (A) shows four days of irradiance measured immediately below the sea-ice. The dashed line is the Ik value determined from the
P/I curve and indicates that light saturation is present over much
of the daylight hours. (B) shows the 24 h integrated O2 fluxes calculated from the irradiances in (A) and Eqs. 3 and 4. The small
variations between these daily O2 fluxes resulted from the ice algae
being light saturated over most of the day.
0.002, p = 0.833), temperature (R 2 = 0.022, p = 0.450), light
(R 2 = 0.016, p = 0.492), or current direction (R 2 = 0.001, p
= 0.866). The O2 flux that was calculated based on the ice
melt rates, through the melting of de-oxygenated ice, averaged −0.28 ± 0.03 mmol O2 m−2 d−1 (SE, n = 31). This flux
amounts to less than 13 % of the daily R rate derived from
fitting Eqs. 3 and 4 to the data in Fig. 5.
The footprint, or the area of the ice surface that contributes
90 % of the measured flux (Fig. 9), was calculated from estimated values of the friction velocity (u*), the surface roughness (zo ), and the measuring height as described by Berg et
al., (2007). This first order calculation revealed a value of
u* of 0.098 ± 0.007 cm s−1 (SE, n = 20) and a hydraulically
smooth ice surface with a zo of 0.023 ± 0.002 cm (SE, n =
20) (see Berg et al., 2007 for details). The oval shaped footprint was about 2.8 m wide by 67 m long with a point of maximum flux contribution at 3.2 m upstream from the eddy correlation instrument (Fig. 9).
The level of anisotropy in the turbulent flow below the seaice, as reflected in the ratios between the eddy diffusivities
(Ex , Ey , and Ez ) in the x, y, and z directions, was estimated
as described by Berg et al., (2007) (Fig. 10). Anisotropy
was clearly evident (Ex /Ez = 6.2 ± 1.0, Ey /Ez = 3.8 ± 0.5,
Ez /Ez = 1 ± 0.0; SE, n = 5).
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Discussion
The eddy correlation technique is in many respects superior
to traditional flux methods (Berg et al., 2003; Kuwae et al.,
2006; Berg and Huettel, 2008) and this study conducted at
the ice-water interface further exhibits the adaptability and
advantages of the technique. The first measurements of O2
eddy correlation fluxes at the ice-water interface presented
here are controlled by ice melt, and to a much larger extent,
the biological activity of the microbial communities. However, the ice-water interface also produces conditions that
must be addressed when sampling eddy correlation ice-water
fluxes such as low current velocities, density stratifications,
and instrument deployment. Despite this, the eddy correlation technique is well suited for flux measurements at the
ice-water interface and has previously been applied and validated for momentum, heat, and salt fluxes (McPhee, 1992;
Shirasawa et al., 1997; Widell et al., 2006; McPhee et al.,
2008).
Measured O2 exchange rates across the ice-water interface
do not directly represent primary production and respiration,
as physical processes may also contribute to the exchange
during periods of rapid freezing or melting. These physical
processes include the release of O2 supersaturated brine during freezing or the release of O2 undersaturated water during
melting (Glud et al., 2002; Rysgaard et al., 2008). Although
these physical processes only contributed a minor portion of
the total O2 flux in this study due to very low melt rates, this
will not always be the case. Heat fluxes can be measured and
translated into melting rates, but the relevant ice O2 concentration, as well as the fraction of the measured heat flux that
is conducted up into the ice rather than melting it, must be
known to accurately calculate the associated O2 flux.
The release of melt or brine flows may also cause stratification immediately below the ice and therefore a careful
examination of the state of turbulence and mixing below the
ice-water interface is required. The eddy correlation technique assumes that the dominant form of vertical transport
is turbulent mixing. Therefore, the technique may produce
inaccurate results at very low current velocities when there
is also insufficient turbulence. Ice freeze and melt may also
cause convection to occur, which has been reported as a problem for measuring O2 fluxes from profiles generated with
microelectrodes (Glud et al., 2002; McMinn et al., 2010).
However, with the eddy correlation technique these convective processes are included in measurements, and with the
concurrent determination of ice melt rates we can estimate
the net O2 flux due to primary production and respiration.
The lack of vertical mixing under low-flow conditions allowed density stratification to build up immediately below
the ice due to ice melt (Fig. 2). This stratification caused the
effects of the flux to be confined within the stratified layer
near the ice, as evidenced by the large pulse in the signal
when the stratification was later eroded (Fig. 2). As a result, accurate fluxes could only be derived when the vertical
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M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Fig. 8. (A) shows the averaged heat fluxes for periods with well-developed turbulence under the ice-water interface. The maximum potential
ice melt rate derived from the heat flux is shown in (B) and the maximum O2 flux calculated from this melt rate is shown in (C). The n
represents the number of 0.25 h measuring periods and error bars represent standard errors.
mixing due to turbulent flow was strong enough to erode
and prevent density stratifications. The mean current velocity provided a benchmark for determining when stratification
would occur, and this was further evaluated by examining the
cumulative flux, as little or no flux was recorded during periods of weak turbulence, while stable and linear cumulative
fluxes were derived during periods of well-developed turbulence (Fig. 2). The presence of well-developed turbulence
was also examined through the spectra of the vertical velocity and the O2 concentration (Fig. 3a and b). The presence
of the inertial sub-range with a characteristic −5/3 slope is
indicative of well-developed turbulence (Kundu and Cohen,
2008). The cospectrum of the vertical velocity and O2 concentration was also examined to determine the frequency of
the turbulent eddies that contributed to the flux. The example
shown in Fig. 3c indicated that the contributing eddies had
frequencies less than 0.1 Hz, and that larger eddies, with a
significant portion below the initial sub-range, were responsible for the vertical transport.
An examination of the eddy diffusivities (Fig. 10) revealed that the turbulent mixing below the ice was clearly
anisotropic as expected in flows near solid boundaries. The
relatively small ratio between the longitudinal (Ex ) and vertical (Ez ) eddy diffusivity of 6:1 suggests that the water column was fully mixed, at least within the upper meters below
the ice-water interface. For comparison, a ratio of 12:1 was
found for a 1 m deep river that was fully vertically mixed
(Berg et al., 2007). If stratification had been present under
the ice it would reduce the vertical mixing and therefore give
a much larger ratio. However, we stress that both the strength
and depth of the density stratification are highly dynamic
properties that change across time, and that intermittent lowflow conditions represent a challenge for eddy correlation
measurements at the ice-water interface.
The footprint of the eddy correlation technique (Fig. 9) is
very large compared to that of other flux methods (Berg et al.,
2007) and is expected to integrate most variability commonly
observed at the ice-water interface (McMinn and Ashworth,
Biogeosciences, 9, 1957–1967, 2012
Fig. 9. The estimated length, width and shape of the eddy correlation footprint are shown in (A, B), and (C), respectively. The error
bars represent standard error and n represents the number of 0.25 h
measuring periods. (C) shows the footprint or the area that contributes 90 % of the flux (note the difference in axis scales). The
open circle represents location of the eddy correlation instrument
and the solid circle indicates the point of maximum flux contribution.
1998; Rysgaard et al., 2001). It has been suggested that to include all horizontal variations in light, algal biomass and algal activity it is necessary to measure across distances of 20
to 100 m as dictated by variations in light availability below
melting ponds and snow drifts (Gosselin et al., 1986; Rysgaard et al., 2001; Søgaard et al., 2010). Figure 9 shows the
size of the footprint to be over 60 m long, and therefore it integrates over most of this variability. However, given the narrow width of the footprint its composition will change with
even a small change in current direction. This variability can
easily be investigated by examining the correlations between
the fluxes and the current direction, and in this study none
were found (R 2 = 0.021, n = 166).
www.biogeosciences.net/9/1957/2012/
M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
Fig. 10. Level of anisotropy in the turbulent flow below the sea-ice,
as reflected in the ratios between the eddy diffusivities (Ex , Ey , and
Ez ) in the x, y, and z directions.
Given the small difference between the mean seawater
temperature and its freezing point, low rates of ice melt were
expected. Throughout the study period low ice melt was observed (Fig. 8), and the measured O2 exchange rates indicated a net consumption of O2 (Fig. 5), which represent both
the consumption by organisms and to a lesser degree the
melting of deoxygenated ice crystals (Glud et al., 2002; Rysgaard and Glud, 2004). Specifically, the O2 flux associated
with ice melting was shown to be less than 13 % of the average daily respiration rate (see Figs. 5 and 8). This calculated potential O2 flux due to melting assumes that all of the
heat flux results in ice melting and that melt water is anoxic.
The O2 concentration of water released during melting remains an uncertainty, especially considering that brine channels may advect O2 depleted or enriched water from anywhere within the ice. The ice also had a significantly lower
temperature than that of the water column (McGinnis et al.,
2012) and therefore the conduction of heat into the colder
ice likely accounted for some heat flux without causing melt.
Considering these uncertainties we were only able to put an
upper limit on how much ice melt could affect the total O2
flux. Thus, this maximum O2 flux due to ice melt cannot
account for any significant differences in fluxes during the
study period. However, it is expected that physical processes
during rapid ice growth and melting during other periods of
the year may greatly affect the O2 exchange and could potentially exceed the biological contribution. The O2 flux showed
a strong correlation to light (Fig. 5) as GPP reduced the net
flux of O2 toward the ice during the day. However, at all
times the ice was net heterotrophic, which has been observed
by others (Rysgaard and Glud, 2004; Rysgaard et al., 2008).
The calculation of GPP uses the standard convention that the
daytime and nighttime respiration rates are equal, as it is not
possible to separate the two. However, this may lead to an
underestimate of daytime respiration due to the stimulation
of respiration by the release of photosynthates (Fenchel and
Glud, 2000; Glud et al., 2009).
www.biogeosciences.net/9/1957/2012/
1965
The P/I curve established through model fitting (Figs. 5
and 6) revealed that the ice algae were well adapted to low
light conditions with an Ik of about 4 µmol photons m−2 s−1 .
This is supported by many other studies that show sea-ice
algae to be low-light adapted with values of Ik between 0.5
and 35 µmol photons m−2 s−1 (Cota, 1985; Kuhl et al., 2001;
Rysgaard et al., 2001). Based on continuously measured light
levels under the ice and the Ik value, the primary producers appear to be light saturated for 75 % of the day below
the snow-covered ice. From the PAR measurements and the
model fit to the derived O2 fluxes (Fig. 5, R 2 = 0.923) it
was possible to estimate the net O2 flux throughout several days including periods of insufficient turbulent mixing
(Fig. 7). Because the primary producers were light saturated
for most of the day, other variables could potentially control
ice algae production. Gradinger (2009) found that nutrients
were the major limiting factors for sea-ice algae production
while others suggest that transient photoinhibition could occur at higher PAR levels (Kuhl et al., 2001; Rysgaard et al.,
2001) or that high salinities may reduce primary production
(Mock and Gradinger, 1999; Mock et al., 2003; Manes and
Gradinger, 2009).
Our GPP rates are supported by three recent studies at
comparable sites in southwest Greenland by Mikkelsen et
al., (2008) and Søgaard et al., (2010, 2012) who estimated
maximum GPP rates using the 14 C tracer method. The first
two studies were conducted in March through April and
the third was conducted parallel with our eddy correlation
measurements at a location approximately 100 m away from
the eddy correlation site. The 14 C method is drastically different from the eddy correlation technique because the ice
is disturbed by melting the ice cores, the cores cover a
much smaller and different ice area, and they are incubated
ex situ for a specific time period. However, all three studies gave similar maximum GPP rates of 1.75, 1.39, and
1.46 mmol O2 m−2 d−1 (assuming a 1:1 ratio of C:O2 for
comparison purposes), respectively, which compared surprisingly well with our estimate of 1.76 mmol O2 m−2 d−1 .
The average GPP rates in March varied between the studies from 0.07 mmol O2 m−2 d−1 (Søgaard et al., 2010) to
1.75 mmol O2 m−2 d−1 (Mikkelsen et al., 2008) and our measurement of average GPP falls within this range (0.69 mmol
O2 m−2 d−1 ). However, the average GPP rate determined
at the same time and location by Søgaard et al., (2012) of
0.30 mmol O2 m−2 d−1 was lower than our rate. Given the
differences in methods we are not surprised by these differences in the results; a point that is further confirmed by bacterial production rates by Søgaard et al., (2010, 2012) using
the 3 H thymidine incorporation method. The estimated respiration rates from 3 H thymidine incubations, which exclude
microalgal respiration and involve a number of other assumptions that vary significantly in the literature (see Søgaard et
al., 2010), of −0.16 and −0.21 mmol O2 m−2 d−1 , respectively, were an order of magnitude less than the respiration
rate of −2.13 mmol O2 m−2 d−1 we found. We believe that
Biogeosciences, 9, 1957–1967, 2012
1966
M. H. Long et al.: Oxygen exchange and ice melt by eddy correlation
these rates are highly dependent on the method used and that
to some degree the differences are caused by the intrusive
nature of slurry incubations in altering important parameters such as O2 , nutrients, and salinity and their distributions
within the ice. Furthermore, tracer incubations and eddy correlation may integrate the activity of different sections of the
sea-ice and are conducted over different time scales. These
differences need further investigation as they have significant
implications for determining carbon turn-over in sea-ice.
In summary, the combined temperature and O2 eddy correlation flux measurements made it possible to evaluate and
separate O2 exchange rates at the ice-water interface due to
biological production and respiration from those caused by
melting. In the future these rates may be further elucidated
by eddy correlation salt fluxes for which fast and reliable sensors are available. We see eddy correlation measurements as
an important tool in future studies of sea-ice metabolism as
they represent the closest we can currently come to truly in
situ measurements.
Acknowledgements. This study received financial support from
the Danish Agency for Science, Technology and Innovation; the
Canada Excellence Research Chair (CERC) program; and the
National Science Foundation, Chemical Oceanography, grant
OCE-0536431. The study is a part of the Greenland Climate
Research Center’s activities (GCRC6507).
Edited by: J. Middelburg
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